Malignant melanoma is currently one of the leading cancers among many light-skinned populations around the world. Changes in recreational behavior together with the increase in ultraviolet radiation due to thinning or lost of the earth's ozone layer have caused a dramatic increase in the number of melanomas diagnosed. The rise in incidence was first noticed in the United States in 1930, where one person out of 100,000 per year suffered from skin cancer. This rate increased in the mid-eighties to six per 100,000 and to 13 per 100,000 in 1991. In fact, melanoma is currently one of the most common cancers in young adults. Each year, more than 50,000 people in the U.S. learn that they have melanoma. According to the World Health Organization website, 132,000 new cases of melanoma skin cancer occur globally each year. One in every three cancers diagnosed is a skin cancer and, according to Skin Cancer Foundation Statistics, one in every five Americans will develop skin cancer during their lifetime. Melanoma accounts for about three percent of skin cancer cases, but it causes more than 75 percent of skin cancer deaths. According to the National Cancer Institute, 68,720 new cases and 8,650 deaths from melanoma occurred in the United States in 2009. According to the Vanderbilt Ingram cancer center, Melanoma is among the 5 top most frequently treated cancers (among 20,000 patients).
The importance of early detection of melanoma cannot be overstated. When melanoma is found and treated early, the chances for long-term survival are excellent. Five-year survival rates for patients with early-stage (Stage I) melanoma exceed 90 to 95%. As melanoma progresses, it becomes increasingly more devastating and deadly. In later-stage disease, 5-year survival rates drop to less than 50%. With early detection, however, survival rates have improved steadily in recent years, and 85% of diagnosed patients enjoy long-term survival after simple tumor surgery.
Melanoma starts in the pigment-producing skin cells (melanocytes). The first sign of melanoma is often a change in the size, shape, or color of an existing mole or the appearance of a new mole. Since the vast majority of primary melanomas are visible on the skin, there is a good chance of detecting the disease in its early stages. If not detected at treated at an early stage, these cells become abnormal, grow uncontrollably, and aggressively invade surrounding tissues. Melanoma can spread quickly and produce large malignant tumors in the brain, lung, liver, or other organs, with depth of penetration being predictive of prognosis: Epidermis only: Clark level I. Upper dermis: Clark levels II and II. Lower dermis: Clark level IV. Fatty layers: Clark level V.
Most tumors of the skin are not cancerous and rarely turn into cancer. Skin cancers are divided into non-melanomas and melanomas. Non-melanomas (usually basal cell and squamous cell cancers) are the most common cancers of the skin. Because they rarely spread elsewhere in the body, they are less worrisome than melanomas. Melanoma is much less common than basal cell and squamous cell skin cancers, but it is far more serious. Because it begins in the melanocytes, most of these cells keep on making melanin thus melanoma tumors are often brown or black (but not always). Melanoma most often appears on the trunk of fair-skinned men and on the lower legs of fair-skinned women, but it can appear in other places as well.
The gold standard for accurate diagnosis remains histological examination of biopsies. The type of biopsy depends on the size of the skin growth and its location on the body. Several types of biopsy can be done when melanoma is suspected. The first is an excisional biopsy, which cuts away the entire growth with a margin of normal surrounding skin. A second type is an incisional biopsy, or core biopsy, removing only a sample of the growth. A punch biopsy removes a small, cylindrical shaped sample of skin. A fourth type is a saucerization biopsy, which removes the entire lesion by cutting under the lesion in a “scoop like” manner. A fifth type is a fine-needle aspiration biopsy done with a very thin needle, which removes a very small sample of tissue (usually not done on moles but on other deeper tissue, such as nearby lymph nodes). Prognosis is assessed by the TNM system (T stands for tumor thickness and how far it has spread; N stands for lymph nodes, and whether the tumor has spread to the nodes; and M stands of metastasis, and whether the tumor has spread to distant organs).
Melanoma may also be diagnosed, to some extent, from the appearance of the skin surface. Four main features of the appearance are used: asymmetry, uneven edges, multiple shades, and size. These characteristics, known as the “ABCD” characteristics, provide a subjective means for physicians and patients to identify pigmented skin lesions that could be melanoma. The four parameters represented by the ABCD characteristics are lesion asymmetry (A), border irregularity (B), color variegation (C) and lesion diameter (D). Currently, experienced dermatologists can identify a melanoma with around 75% accuracy (Serruys, 1999).
The ability to identify most melanomas visually suggests that digital images and computer based image analysis may be effective tools for rapid screening. One example of such a tool is the MelaFind® scanner from Electro-Optical Sciences, Inc. (Irvington, N.Y.), aspects of which are described in U.S. Pat. No. 6,081,612, U.S. Pat. No. 6,208,749, U.S. Pat. No. 6,307,957, U.S. Pat. No. 6,563,616, U.S. Pat. No. 6,626,558, U.S. Pat. No. 6,657,798, U.S. Pat. No. 6,710,947, U.S. Pat. No. 7,102,672, and U.S. Pat. No. 7,127,094, filed Jan. 2, 2003, and U.S. Patent Publications No. 2008/0031537, No. 2008/0214907, No. 2008/0312952, No. 2009/0060304 and No. 2009/0154781, each incorporated here by reference. The MelaFind® scanner is a large hand-held scanner housing a multi-spectral light source and a sensor that is placed directly in contact with the lesion. The MelaFind® scanner is designed for use by medical professionals and is not intended for general consumer use, which means that the patient must have already suspected a problem and consulted a physician before such a scanner would be available for use on the patient.
Reported efforts to develop methods for machine-based diagnosis of melanoma using digital images include a number of pre-processing steps, such as standardizing illumination, shading correction, noise filtering for color quality and use of polarizing filters. The image resolution varies from study to study, but typically is not lower than 256×256 pixel images, with 0.01 cm/pixel and 24 bit per pixel color depth. Some methods remove hair by image processing, while others involve shaving the patients around the lesion before taking the photograph.
Accessibility of machine-based diagnosis can be extended by using everyday digital images, such as images taken using the built-in camera of a smart phone or a simple digital camera. Such an approach would make melanoma screening more accessible to individuals who are concerned about the health of their skin but have not yet been able to consult a physician. However, the quality of such images tends to be fairly low.
Optimized extraction and reconstruction of data within an image can be problematic where sources of noise and other factors can negatively impact the ability to efficiently extract data from the image, thus impairing the effectiveness of the imaging method for its intended use. Examples of areas in which image analysis can be problematic include astronomical observation and planetary exploration, where sources can be faint and atmospheric interference introduce noise and distortion, military and security surveillance, where light can be low and rapid movement of targets result in low contrast and blur, and medical imaging, which often suffers from low contrast, blur and distortion due to source and instrument limitations. Adding to the difficulty of image analysis is the large volume of data contained within a digitized image, since the value of any given data point often cannot be established until the entire image is processed.
Development of methods for automated analysis of digital images has received considerable attention over that past few decades, with one of the key areas of interest being the medical field. Applications include analysis of pathology images generated using visual, ultrasound, x-ray, positron emission, magnetic resonance and other imaging methods. As in the case of human-interpreted medical images, an automated image analyzer must be capable of recognizing and classifying blurred features within the images, which often requires discrimination of faint boundaries between areas differing by only a few gray levels or shades of color.
In recent years, machine-learning approaches for image analysis have been widely explored for recognizing patterns which, in turn, allow extraction of significant features within an image from a background of irrelevant detail. Learning machines comprise algorithms that may be trained to generalize using data with known outcomes. Trained learning machine algorithms may then be applied to predict the outcome in cases of unknown outcome. Machine-learning approaches, which include neural networks, hidden Markov models, belief networks and support vector machines, are ideally suited for domains characterized by the existence of large amounts of data, noisy patterns and the absence of general theories. Particular focus among such approaches has been on the application of artificial neural networks to biomedical image analysis, with results reported in the use of neural networks for analyzing visual images of cytology specimens and mammograms for the diagnosis of breast cancer, classification of retinal images of diabetics, karyotyping (visual analysis of chromosome images) for identifying genetic abnormalities, and tumor detection in ultrasound images, among others.
The majority of learning machines that have been applied to image analysis are neural networks trained using back-propagation, a gradient-based method in which errors in classification of training data are propagated backwards through the network to adjust the bias weights of the network elements until the mean squared error is minimized. A significant drawback of back-propagation neural networks is that the empirical risk function may have many local minimums, a case that can easily obscure the optimal solution from discovery. Standard optimization procedures employed by back-propagation neural networks may converge to a minimum, but the neural network method cannot guarantee that even a localized minimum is attained, much less the desired global minimum. The quality of the solution obtained from a neural network depends on many factors. In particular, the skill of the practitioner implementing the neural network determines the ultimate benefit, but even factors as seemingly benign as the random selection of initial weights can lead to poor results. Furthermore, the convergence of the gradient-based method used in neural network learning is inherently slow. A further drawback is that the sigmoid function has a scaling factor, which affects the quality of approximation. Possibly the largest limiting factor of neural networks as related to knowledge discovery is the “curse of dimensionality” associated with the disproportionate growth in required computational time and power for each additional feature or dimension in the training data.
The shortcomings of neural networks can be overcome by using the support vector machine. In general terms, a support vector machine (SVM) maps input vectors into high dimensional feature space through a non-linear mapping function, chosen a priori. In this high dimensional feature space, an optimal separating hyperplane is constructed. The optimal hyperplane is then used to perform operations such as class separations, regression fit, or density estimation. SVMs are well-recognized as having the advantage in solving classification problems of high dimension and small size dataset.
U.S. Pat. Nos. 6,157,921, 6,714,925, and 7,797,257, which are incorporated herein by reference, describe a system and method for providing SVM analysis services for processing of data transmitted from a remote source over the Internet to a processor that executes trained SVMs. The processor receives the data from the remote source along with account information that provides for a financial transaction to secure payment for the analysis services. Upon completion of the data processing, the analysis results are transmitted to the remote requestor over the Internet and a transaction is initiated, for example with a financial institution, to secure payment for the data analysis services from the designated account.
In view of the serious nature of the disease, and the extreme importance of early detection, a system and method are needed to allow individuals who may be concerned that they have melanoma to obtain a rapid, preliminary screening using a computer-based image analysis and pattern recognition tool that is easily accessible via the Internet and which can utilize readily-available imaging techniques such as a smart phone camera or convention digital camera. The present invention expands the system and method disclosed in the aforementioned patents and applications to provide such a capability.